scikit_learn_ML.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOBJwJpvfJ5k4JpzDRsCV+4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ultrasage-danz/scikit-learn-ML/blob/main/scikit_learn_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wN03_nY2vCrg"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris()\n",
        "type(iris)"
      ],
      "metadata": {
        "id": "KtrvVuBwEi8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.data)"
      ],
      "metadata": {
        "id": "09x8GCQdErVr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.feature_names)"
      ],
      "metadata": {
        "id": "AdrEh88xFUsn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.target)"
      ],
      "metadata": {
        "id": "eqJKrOc2FVCJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.target_names)"
      ],
      "metadata": {
        "id": "a3-hyl6wFVS-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(iris.data))\n",
        "print(type(iris.target))"
      ],
      "metadata": {
        "id": "SBqGfDPoFVkW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.data.shape)"
      ],
      "metadata": {
        "id": "BzXWB4bvFV3P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.target.shape)"
      ],
      "metadata": {
        "id": "EI8SUuSNHLz5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "id": "hIxIE7Y8HMeH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "id": "nY7-6fvpwEAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "Uxp--YwmDP3S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=1)"
      ],
      "metadata": {
        "id": "uK6zKGK2DQIC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(knn)"
      ],
      "metadata": {
        "id": "bFuMFobzDQW6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n"
      ],
      "metadata": {
        "id": "pf-QeDOgDQlR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=1)"
      ],
      "metadata": {
        "id": "MD6hqm56DRaF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(knn)"
      ],
      "metadata": {
        "id": "8-J807SNDRpC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn.fit(X, y)\n"
      ],
      "metadata": {
        "id": "-qz-ni95DR3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn.predict([[3, 5, 4, 2]])"
      ],
      "metadata": {
        "id": "31d07kK0Fa6w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = np.array([[3, 5, 4, 2], [5, 4, 3, 2]])\n",
        "knn.predict(X_new)"
      ],
      "metadata": {
        "id": "u_P9Bh3FFawH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knns = KNeighborsClassifier(n_neighbors=5)\n",
        "knns.fit(X, y)\n",
        "knns.predict(X_new)"
      ],
      "metadata": {
        "id": "BBP8kDD5Fajg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "#set solver to liblinear\n",
        "logreg = LogisticRegression(solver='liblinear')\n",
        "logreg.fit(X, y)\n",
        "logreg.predict(X_new)\n"
      ],
      "metadata": {
        "id": "McmQFJm9LpQs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "\n",
        "X = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "id": "XhhJAmFuLpfb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "#set solver to liblinear\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(X, y)\n",
        "logreg.predict(X)\n"
      ],
      "metadata": {
        "id": "XQ_PHB_ELpuF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = logreg.predict(X)\n",
        "len(y_pred)"
      ],
      "metadata": {
        "id": "r1Bpo_nsXVg1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print(metrics.accuracy_score(y, y_pred))"
      ],
      "metadata": {
        "id": "E2KLWch8XWP2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X, y)\n",
        "y_pred = knn.predict(X)\n",
        "print(metrics.accuracy_score(y, y_pred))"
      ],
      "metadata": {
        "id": "vck38D7IXW3D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "knn.fit(X, y)\n",
        "y_pred = knn.predict(X)\n",
        "print(metrics.accuracy_score(y, y_pred))"
      ],
      "metadata": {
        "id": "lJmz3gbxXXfp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=150)\n",
        "knn.fit(X, y)\n",
        "y_pred = knn.predict(X)\n",
        "print(metrics.accuracy_score(y, y_pred))"
      ],
      "metadata": {
        "id": "JwaAknsyXYFl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot a line chart based on the accuracy score of the metrics for evry +10 increase in the n_neighbours for the KNeighborsClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# try n_neighbors from 1 to 150 with step 10\n",
        "k_range = range(1, 151, 1)\n",
        "scores = []\n",
        "\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X, y)\n",
        "    y_pred = knn.predict(X)\n",
        "    scores.append(metrics.accuracy_score(y, y_pred))\n",
        "\n",
        "# plot the relationship between K and testing accuracy\n",
        "plt.plot(k_range, scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Testing Accuracy')"
      ],
      "metadata": {
        "id": "EQsgCgOq1AQ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Accuracy Score declines constantly until sample 99 where it takes a steep dive"
      ],
      "metadata": {
        "id": "3GsRwY2J02y7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#plot a line chart based on the accuracy score of the metrics for evry +10 increase in the n_neighbours for the KNeighborsClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# try n_neighbors from 1 to 150 with step 10\n",
        "k_range = range(99, 101, 1)\n",
        "scores = []\n",
        "\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X, y)\n",
        "    y_pred = knn.predict(X)\n",
        "    scores.append(metrics.accuracy_score(y, y_pred))\n",
        "\n",
        "# plot the relationship between K and testing accuracy\n",
        "plt.plot(k_range, scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Testing Accuracy')"
      ],
      "metadata": {
        "id": "WJ60UoAhXYqW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=4)"
      ],
      "metadata": {
        "id": "PfU8XSTiXZJu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "id": "7bwcmw_BXZrL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "id": "hrWji-f81uOF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train the model on the training set\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "-HadGIYD1ucs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = logreg.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "zgJTjaKf1uo1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "Y8yaxMNF1u0o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "caOQtPRC1vA9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# try n_neighbors from 1 to 150 with step 10\n",
        "k_range = range(1, 91, 10)\n",
        "scores = []\n",
        "\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred = knn.predict(X_test)\n",
        "    scores.append(metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "# plot the relationship between K and testing accuracy\n",
        "plt.plot(k_range, scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Testing Accuracy')"
      ],
      "metadata": {
        "id": "7lcB_j7p1vNN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# try n_neighbors from 1 to 150 with step 10\n",
        "k_range = range(45, 91, 1)\n",
        "scores = []\n",
        "\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred = knn.predict(X_test)\n",
        "    scores.append(metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "# plot the relationship between K and testing accuracy\n",
        "plt.plot(k_range, scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Testing Accuracy')"
      ],
      "metadata": {
        "id": "5yWIcDvL1vYi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# try n_neighbors from 1 to 150 with step 10\n",
        "k_range = range(51, 91, 1)\n",
        "scores = []\n",
        "\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred = knn.predict(X_test)\n",
        "    scores.append(metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "# plot the relationship between K and testing accuracy\n",
        "plt.plot(k_range, scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Testing Accuracy')"
      ],
      "metadata": {
        "id": "GE7E4Md51vjl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=11)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "wZZN0Z0n1vt7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k_range = range(1, 26)\n",
        "scores = []\n",
        "\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred = knn.predict(X_test)\n",
        "    scores.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "0sa0thXt1v5g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "# plot the relationship between K and testing accuracy\n",
        "plt.plot(k_range, scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Testing Accuracy')"
      ],
      "metadata": {
        "id": "ydo9fEQG1wDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=11)\n",
        "knn.fit(X, y)\n",
        "\n",
        "knn.predict([[3, 5, 4, 2]])"
      ],
      "metadata": {
        "id": "PFrjfyHM1wOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "m7d39lJb1wZW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#read from Advertising.csv\n",
        "data = pd.read_csv('Advertising.csv', index_col=0)"
      ],
      "metadata": {
        "id": "ch8tmjQDMeEj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "id": "oLeFr92I1wuT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "sns.pairplot(data, x_vars=['TV', 'Radio', 'Newspaper'], y_vars='Sales', size=7, aspect=0.7, kind='reg')"
      ],
      "metadata": {
        "id": "d3lOZ5dd1w4-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "0fa9a23b-1fba-45b5-baab-860fd58ff213"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:2100: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7af4c1e66d70>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1470x700 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols = ['TV', 'Radio', 'Newspaper']\n",
        "X = data[feature_cols]\n",
        "\n",
        "print(X.shape)\n",
        "print(type(X))"
      ],
      "metadata": {
        "id": "vnIeMTrd1xNi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6bcae33-92c4-4319-bcd4-68879f15b472"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(200, 3)\n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = data['Sales']\n",
        "print(y.shape)\n",
        "print(type(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1uRZ-WCTHIF",
        "outputId": "5768807d-ca6f-43ed-99b5-f31d0efaedfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(200,)\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
      ],
      "metadata": {
        "id": "zS3dc7vkT81F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wby7dnu4T9OK",
        "outputId": "06e5da42-4d8e-4fcb-9f95-48fe3b153278"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 3)\n",
            "(50, 3)\n",
            "(150,)\n",
            "(50,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "linreg = LinearRegression()\n",
        "linreg.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "J4U_NsMGT9nQ",
        "outputId": "a91cfd1a-e8e7-4b40-a3fc-2123fb7d6e02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(linreg.intercept_)\n",
        "print(linreg.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "whJuToUdT96x",
        "outputId": "8d779661-4fed-4b69-bc7c-6d0aa405a484"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.87696662231793\n",
            "[0.04656457 0.17915812 0.00345046]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(zip(feature_cols, linreg.coef_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9bR5lmXT-Jf",
        "outputId": "17a9f99e-4278-4b08-b71a-200d28e8b71c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('TV', 0.046564567874150295),\n",
              " ('Radio', 0.17915812245088839),\n",
              " ('Newspaper', 0.0034504647111804343)]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = linreg.predict(X_test)\n",
        "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tiHkZ0NqT-Yg",
        "outputId": "a9f3f2be-2459-4257-d958-a3468edd1185"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.404651423032895\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols = ['TV', 'Radio']\n",
        "X = data[feature_cols]\n",
        "\n",
        "y = data['Sales']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
        "linreg.fit(X_train, y_train)\n",
        "y_pred = linreg.predict(X_test)\n",
        "\n",
        "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTcUd3J3T-3X",
        "outputId": "0dc4c4ef-eeda-4b47-ca1a-720a08b28489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3879034699382888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "FgYvELQGT_SM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "id": "pz2Ij370oROr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "koDnscY8T_00",
        "outputId": "8a0e6a59-95da-48b7-d5f0-97d20261f184"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_range = range(1, 101)\n",
        "scores = []\n",
        "\n",
        "for k in k_range:\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=k)\n",
        "\n",
        "    knn = KNeighborsClassifier(n_neighbors=5)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred = knn.predict(X_test)\n",
        "    scores.append(metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "# plot the relationship between K and testing accuracy\n",
        "plt.plot(k_range, scores)\n",
        "plt.xlabel('Value of random_state')\n",
        "plt.ylabel('Testing Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "-vYr-erDUACO",
        "outputId": "6eae98de-018c-4e0b-9b27-dc9b7e2b88a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Testing Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#simulate splitting a dataset of 25 observations into 5 folds\n",
        "from sklearn.model_selection import KFold\n",
        "kf = KFold(n_splits=5, shuffle=False).split(range(25))\n",
        "\n",
        "#print the contents of each training and testing set\n",
        "print('{} {:^61} {}'.format('Iteration', 'Training set observations ', 'Testing set observations'))\n",
        "for iteration, data in enumerate(kf, start=1):\n",
        "    print('{:^9} {} {:^25}'.format(iteration, data[0], str(data[1])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diZ3yI0VUASs",
        "outputId": "cf684920-7d31-4517-83c1-cef5a5272ac0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration                  Training set observations                    Testing set observations\n",
            "    1     [ 5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]        [0 1 2 3 4]       \n",
            "    2     [ 0  1  2  3  4 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]        [5 6 7 8 9]       \n",
            "    3     [ 0  1  2  3  4  5  6  7  8  9 15 16 17 18 19 20 21 22 23 24]     [10 11 12 13 14]     \n",
            "    4     [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 20 21 22 23 24]     [15 16 17 18 19]     \n",
            "    5     [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]     [20 21 22 23 24]     \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "-P6cW0Vt5PdB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
        "print(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rNeQsRPoDkW",
        "outputId": "a76fffd6-7fe3-4cde-a844-405d6820ae6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.         0.93333333 1.         1.         0.86666667 0.93333333\n",
            " 0.93333333 1.         1.         1.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(scores.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxSYyeMSoDvL",
        "outputId": "7058bf49-141e-4c0b-ef7d-306e0c599ec5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9666666666666668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_range = range(1, 31)\n",
        "k_scores = []\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
        "    k_scores.append(scores.mean())\n",
        "print(k_scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSWRaKVDoD5v",
        "outputId": "4562a337-5b9b-4ad1-eb5c-c3fb818c2dc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.96, 0.9533333333333334, 0.9666666666666666, 0.9666666666666666, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9800000000000001, 0.9666666666666666, 0.9666666666666666, 0.9733333333333334, 0.96, 0.9666666666666666, 0.96, 0.9666666666666666, 0.9533333333333334, 0.9533333333333334, 0.9533333333333334]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "# plot the value of K for KNN (x-axis) versus the cross-validated accuracy (y-axis)\n",
        "plt.plot(k_range, k_scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "hbR6lhjdoEEY",
        "outputId": "6b719c48-fe46-4017-c606-b88ad15d1fa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cross-Validated Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=20)\n",
        "print(cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YaZ6sBEWoEN5",
        "outputId": "d5130e54-20c2-46cf-e347-d10fa7e2b1dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9800000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg = LogisticRegression()\n",
        "print(cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzgKX_RGoEZx",
        "outputId": "36d93b6f-6e64-41c8-b82e-16e60855d16b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9733333333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n"
      ],
      "metadata": {
        "id": "qj1XcGoYoEku"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('Advertising.csv', index_col=0)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qJym5qh9oEvC",
        "outputId": "bf1c184a-1cfb-4e07-a10e-7fd921d3ae64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      TV  Radio  Newspaper  Sales\n",
              "1  230.1   37.8       69.2   22.1\n",
              "2   44.5   39.3       45.1   10.4\n",
              "3   17.2   45.9       69.3    9.3\n",
              "4  151.5   41.3       58.5   18.5\n",
              "5  180.8   10.8       58.4   12.9"
            ],
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              "      <th>TV</th>\n",
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              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"TV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.85423631490808,\n        \"min\": 0.7,\n        \"max\": 296.4,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          287.6,\n          286.0,\n          78.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Radio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.846809176168724,\n        \"min\": 0.0,\n        \"max\": 49.6,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          8.2,\n          36.9,\n          44.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Newspaper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.778620838522826,\n        \"min\": 0.3,\n        \"max\": 114.0,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          22.3,\n          5.7,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.217456565710478,\n        \"min\": 1.6,\n        \"max\": 27.0,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          11.4,\n          21.2,\n          12.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols = ['TV', 'Radio', 'Newspaper']\n",
        "X = data[feature_cols]\n",
        "y = data['Sales']"
      ],
      "metadata": {
        "id": "HBls_WgAoE4-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lm = LinearRegression()\n",
        "scores = cross_val_score(lm, X, y, cv=10, scoring='neg_mean_squared_error')\n",
        "print(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6DYZMS3oFDV",
        "outputId": "a4c9f114-38ba-4092-c9b6-462d5feb629e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-3.56038438 -3.29767522 -2.08943356 -2.82474283 -1.3027754  -1.74163618\n",
            " -8.17338214 -2.11409746 -3.04273109 -2.45281793]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse_scores = -scores\n",
        "print(mse_scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hoWZ1RjAoFRL",
        "outputId": "4ab77269-9196-4554-9fe7-6b2a4ae986f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.56038438 3.29767522 2.08943356 2.82474283 1.3027754  1.74163618\n",
            " 8.17338214 2.11409746 3.04273109 2.45281793]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_scores = np.sqrt(mse_scores)\n",
        "print(rmse_scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B8a8k-DxoFhU",
        "outputId": "d4d923c6-fb3f-4dc3-8a30-36f235639728"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.88689808 1.81595022 1.44548731 1.68069713 1.14139187 1.31971064\n",
            " 2.85891276 1.45399362 1.7443426  1.56614748]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_scores.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4o6k8G7NoFr8",
        "outputId": "f9874b83-8d83-4c9d-d6ae-aa8f89a625d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.6913531708051797"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols = ['TV', 'Radio']\n",
        "X = data[feature_cols]\n",
        "print(np.sqrt(-cross_val_score(lm, X, y, cv=10, scoring='neg_mean_squared_error')).mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9iHRXxsoF8L",
        "outputId": "e2a18706-4c84-4df3-e4ee-2e04dc8140d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.6796748419090761\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "id": "Wfb_zLIZoGXD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
        "print(scores)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpVPJLjuoG4F",
        "outputId": "0fddc312-d2b4-43d6-e3ee-8ae0c3fd6192"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.         0.93333333 1.         1.         0.86666667 0.93333333\n",
            " 0.93333333 1.         1.         1.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(scores.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8p5kwXroHH9",
        "outputId": "0ae4fc97-1715-44a2-a651-f93561685039"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9666666666666668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_range = range(1, 31)\n",
        "k_scores = []\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
        "    k_scores.append(scores.mean())"
      ],
      "metadata": {
        "id": "Cei2BuSFoHTM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(k_range, k_scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "wPf-cTL8Nn3e",
        "outputId": "d2a4cadc-6aab-4c0c-c30b-6a6bf668a309"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cross-Validated Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "Ftffm3SENoDe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k_range = range(1, 31)\n",
        "print(k_range)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fJ3z13ZNoPh",
        "outputId": "88600c35-cfd6-4769-a7a1-80f263f6dfcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "range(1, 31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = dict(n_neighbors=k_range)\n",
        "print(param_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TirMq04FNocF",
        "outputId": "0b1ca3fb-8205-46ff-c95d-a90f4f35506a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_neighbors': range(1, 31)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')"
      ],
      "metadata": {
        "id": "FJnCGX6zNosu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "K_zlcfDDNpEw",
        "outputId": "54e00195-baf6-419b-fd83-d35832bd5517"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=30),\n",
              "             param_grid={'n_neighbors': range(1, 31)}, scoring='accuracy')"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=30),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: range(1, 31)}, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=30),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: range(1, 31)}, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=30)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=30)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert the search results into a pandas DataFrame\n",
        "import pandas as pd\n",
        "results = pd.DataFrame(grid.cv_results_)\n"
      ],
      "metadata": {
        "id": "1kJAVcFGNpWz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# view the mean and standard deviation of the test scores for each set of parameters\n",
        "results[['mean_test_score', 'std_test_score', 'params']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "JJh041WGNpj5",
        "outputId": "c5663c5b-9b7d-4e99-c23c-29fb58849b39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    mean_test_score  std_test_score               params\n",
              "0          0.960000        0.053333   {'n_neighbors': 1}\n",
              "1          0.953333        0.052068   {'n_neighbors': 2}\n",
              "2          0.966667        0.044721   {'n_neighbors': 3}\n",
              "3          0.966667        0.044721   {'n_neighbors': 4}\n",
              "4          0.966667        0.044721   {'n_neighbors': 5}\n",
              "5          0.966667        0.044721   {'n_neighbors': 6}\n",
              "6          0.966667        0.044721   {'n_neighbors': 7}\n",
              "7          0.966667        0.044721   {'n_neighbors': 8}\n",
              "8          0.973333        0.032660   {'n_neighbors': 9}\n",
              "9          0.966667        0.044721  {'n_neighbors': 10}\n",
              "10         0.966667        0.044721  {'n_neighbors': 11}\n",
              "11         0.973333        0.032660  {'n_neighbors': 12}\n",
              "12         0.980000        0.030551  {'n_neighbors': 13}\n",
              "13         0.973333        0.044222  {'n_neighbors': 14}\n",
              "14         0.973333        0.032660  {'n_neighbors': 15}\n",
              "15         0.973333        0.032660  {'n_neighbors': 16}\n",
              "16         0.973333        0.032660  {'n_neighbors': 17}\n",
              "17         0.980000        0.030551  {'n_neighbors': 18}\n",
              "18         0.973333        0.032660  {'n_neighbors': 19}\n",
              "19         0.980000        0.030551  {'n_neighbors': 20}\n",
              "20         0.966667        0.033333  {'n_neighbors': 21}\n",
              "21         0.966667        0.033333  {'n_neighbors': 22}\n",
              "22         0.973333        0.032660  {'n_neighbors': 23}\n",
              "23         0.960000        0.044222  {'n_neighbors': 24}\n",
              "24         0.966667        0.033333  {'n_neighbors': 25}\n",
              "25         0.960000        0.044222  {'n_neighbors': 26}\n",
              "26         0.966667        0.044721  {'n_neighbors': 27}\n",
              "27         0.953333        0.042687  {'n_neighbors': 28}\n",
              "28         0.953333        0.042687  {'n_neighbors': 29}\n",
              "29         0.953333        0.042687  {'n_neighbors': 30}"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1afa27d2-34ff-45e4-b7e6-833de7adfa2a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>params</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.053333</td>\n",
              "      <td>{'n_neighbors': 1}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.953333</td>\n",
              "      <td>0.052068</td>\n",
              "      <td>{'n_neighbors': 2}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 3}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 4}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 5}</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 6}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 7}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 8}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.032660</td>\n",
              "      <td>{'n_neighbors': 9}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 10}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 11}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.032660</td>\n",
              "      <td>{'n_neighbors': 12}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.030551</td>\n",
              "      <td>{'n_neighbors': 13}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.044222</td>\n",
              "      <td>{'n_neighbors': 14}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.032660</td>\n",
              "      <td>{'n_neighbors': 15}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.032660</td>\n",
              "      <td>{'n_neighbors': 16}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.032660</td>\n",
              "      <td>{'n_neighbors': 17}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.030551</td>\n",
              "      <td>{'n_neighbors': 18}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.032660</td>\n",
              "      <td>{'n_neighbors': 19}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.030551</td>\n",
              "      <td>{'n_neighbors': 20}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>{'n_neighbors': 21}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>{'n_neighbors': 22}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.973333</td>\n",
              "      <td>0.032660</td>\n",
              "      <td>{'n_neighbors': 23}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.044222</td>\n",
              "      <td>{'n_neighbors': 24}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>{'n_neighbors': 25}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.044222</td>\n",
              "      <td>{'n_neighbors': 26}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.044721</td>\n",
              "      <td>{'n_neighbors': 27}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.953333</td>\n",
              "      <td>0.042687</td>\n",
              "      <td>{'n_neighbors': 28}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.953333</td>\n",
              "      <td>0.042687</td>\n",
              "      <td>{'n_neighbors': 29}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.953333</td>\n",
              "      <td>0.042687</td>\n",
              "      <td>{'n_neighbors': 30}</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1afa27d2-34ff-45e4-b7e6-833de7adfa2a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-1afa27d2-34ff-45e4-b7e6-833de7adfa2a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1afa27d2-34ff-45e4-b7e6-833de7adfa2a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e8a6e8c1-72b1-4020-8c92-72a41d693623\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8a6e8c1-72b1-4020-8c92-72a41d693623')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e8a6e8c1-72b1-4020-8c92-72a41d693623 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"results[['mean_test_score', 'std_test_score', 'params']]\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"mean_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00770132097938961,\n        \"min\": 0.9533333333333334,\n        \"max\": 0.9800000000000001,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.96,\n          0.9533333333333334,\n          0.9800000000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std_test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006881813231385871,\n        \"min\": 0.030550504633038926,\n        \"max\": 0.05333333333333332,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.044221663871405324,\n          0.052068331172711015,\n          0.04422166387140532\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"params\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# examine the results of the first set of parameters\n",
        "results['params'][0]\n",
        "results['mean_test_score'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EcvIJX6rNpyr",
        "outputId": "c6a4822b-06b9-4682-cb99-8df97c854ebf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.96"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# list all of the mean test scores\n",
        "grid_mean_scores = results['mean_test_score']"
      ],
      "metadata": {
        "id": "rvfYh8RpNp-g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(k_range, grid_mean_scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "RwoexUnHNqKo",
        "outputId": "ddf9ae3f-35e8-4e7b-fc3f-186627f870cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cross-Validated Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_score_\n",
        "grid.best_params_\n",
        "grid.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "aItJ0K13NqUs",
        "outputId": "c4505518-f4eb-4ea0-f8f9-ca6e96009a5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=13)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=13)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=13)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_range = range(1, 31)\n",
        "weight_options = ['uniform', 'distance']"
      ],
      "metadata": {
        "id": "qWFFWCacNqeE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = dict(n_neighbors=k_range, weights=weight_options)\n",
        "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n",
        "grid.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "wFequjWUNqnH",
        "outputId": "0488ea6f-fc85-4935-c8b6-ea970ddabdbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=30),\n",
              "             param_grid={'n_neighbors': range(1, 31),\n",
              "                         'weights': ['uniform', 'distance']},\n",
              "             scoring='accuracy')"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=30),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: range(1, 31),\n",
              "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=30),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: range(1, 31),\n",
              "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=30)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=30)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid.best_score_)\n",
        "print(grid.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKjwZtxvNraQ",
        "outputId": "dfb9fc96-a7a1-4d46-ce15-ed7f3ee0c2da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9800000000000001\n",
            "{'n_neighbors': 13, 'weights': 'uniform'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=11, weights='distance')\n",
        "knn.fit(X, y)\n",
        "knn.predict([[3, 5, 4, 2]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n643gz_yNsaC",
        "outputId": "ad27dc75-e350-4dde-865d-0dc8bfa6122b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.predict([[3, 5, 4, 2]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7Ca_2KkNsuN",
        "outputId": "ef9a1b88-ab35-4c8c-9ba2-79ea5ba8e6c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "metadata": {
        "id": "XUR2aWo_Ns7l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_dist = dict(n_neighbors=k_range, weights=weight_options)"
      ],
      "metadata": {
        "id": "1zcMTJp2Nt0k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state=5)\n",
        "rand.fit(X, y)\n",
        "# rand.cv_results_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "rnKJKbfWNuA5",
        "outputId": "93e331cd-f161-44d7-de62-82ded4b6b18d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=10,\n",
              "                   estimator=KNeighborsClassifier(n_neighbors=11,\n",
              "                                                  weights='distance'),\n",
              "                   param_distributions={'n_neighbors': range(1, 31),\n",
              "                                        'weights': ['uniform', 'distance']},\n",
              "                   random_state=5, scoring='accuracy')"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=10,\n",
              "                   estimator=KNeighborsClassifier(n_neighbors=11,\n",
              "                                                  weights=&#x27;distance&#x27;),\n",
              "                   param_distributions={&#x27;n_neighbors&#x27;: range(1, 31),\n",
              "                                        &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "                   random_state=5, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=10,\n",
              "                   estimator=KNeighborsClassifier(n_neighbors=11,\n",
              "                                                  weights=&#x27;distance&#x27;),\n",
              "                   param_distributions={&#x27;n_neighbors&#x27;: range(1, 31),\n",
              "                                        &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "                   random_state=5, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=11, weights=&#x27;distance&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=11, weights=&#x27;distance&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(rand.best_score_)\n",
        "print(rand.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STsoDF4MoHtg",
        "outputId": "bf455f7d-1de2-48ba-f559-dcf8a17bd098"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9800000000000001\n",
            "{'weights': 'uniform', 'n_neighbors': 18}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_scores = []\n",
        "for _ in range(20):\n",
        "    rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10)\n",
        "    rand.fit(X, y)\n",
        "    best_scores.append(round(rand.best_score_, 4))\n",
        "print(best_scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-BRvs45oH8Y",
        "outputId": "0ce3c03e-432f-48fd-b658-4920664076fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.98, 0.98, 0.9733, 0.9733, 0.9733, 0.9733, 0.9733, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.9733, 0.98, 0.98, 0.98, 0.9733]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']\n",
        "# load dataset\n",
        "pima = pd.read_csv(\"pima-indians-diabetes.data\", header=None, names=col_names)"
      ],
      "metadata": {
        "id": "HmbNn4DteWL5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pima.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OVNwD-Q4eWYg",
        "outputId": "72003976-68f9-48c0-cfe3-9921528f8d07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pregnant  glucose  bp  skin  insulin   bmi  pedigree  age  label\n",
              "0         6      148  72    35        0  33.6     0.627   50      1\n",
              "1         1       85  66    29        0  26.6     0.351   31      0\n",
              "2         8      183  64     0        0  23.3     0.672   32      1\n",
              "3         1       89  66    23       94  28.1     0.167   21      0\n",
              "4         0      137  40    35      168  43.1     2.288   33      1"
            ],
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              "        document.querySelector('#df-f21b1609-71d9-4968-86c6-9cded2d32533 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f21b1609-71d9-4968-86c6-9cded2d32533');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9c2f5093-7d0b-4a57-9431-9bc75a0b30ef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pima",
              "summary": "{\n  \"name\": \"pima\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"pregnant\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pedigree\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols = ['pregnant', 'insulin', 'bmi', 'age']\n",
        "X = pima[feature_cols]\n",
        "y = pima.label"
      ],
      "metadata": {
        "id": "n4ahwdKjeWlB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
      ],
      "metadata": {
        "id": "vPayqUtkeWyG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "yAXOIjTaeW-H",
        "outputId": "c261235e-b28a-4984-f913-4f5af1947979"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_class = logreg.predict(X_test)"
      ],
      "metadata": {
        "id": "mVeWk6oieYPi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print(metrics.accuracy_score(y_test, y_pred_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2sd34wieYcY",
        "outputId": "f5a74539-cf1f-4ea2-e4a2-c2eb40b5e786"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6770833333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uZpvA9LdeYot",
        "outputId": "793d62ee-383b-423a-b85c-19b68182905b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "0    130\n",
              "1     62\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7U6mndYVeZxX",
        "outputId": "2940d0ee-b795-43ca-b658-123c226568cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3229166666666667"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "1 - y_test.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7dqRQOAeaA2",
        "outputId": "d8171950-ee74-4c9b-ae3c-d9999285fe2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6770833333333333"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(y_test.mean(), 1 - y_test.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9c2xnL9teaK1",
        "outputId": "0cfcf6c6-9ffb-4fce-e501-ba1a057ca5f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6770833333333333"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts().head(1) / len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kWm3VCrJiY4",
        "outputId": "70e0bc09-42e5-44db-ad91-d9676b70df2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "0    0.677083\n",
              "Name: count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('True', y_test.values[0:25])\n",
        "print('Pred', y_pred_class[0:25])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1UN3JesJimD",
        "outputId": "04387a37-8370-4890-88d9-b01201f7f345"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True [1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0]\n",
            "Pred [0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(y_test, y_pred_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JMPKxGcfJi0g",
        "outputId": "d729a317-abe5-44fa-96be-eaa7b1cc9b18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[114  16]\n",
            " [ 46  16]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from re import T\n",
        "confusion = metrics.confusion_matrix(y_test, y_pred_class)\n",
        "TP = confusion[1, 1]\n",
        "TN = confusion[0, 0]\n",
        "FP = confusion[0, 1]\n",
        "FN = confusion[1, 0]"
      ],
      "metadata": {
        "id": "w_Ay8u0oJjCk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "![Large confusion matrix](09_confusion_matrix.png)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MEHGJIpsJjPE",
        "outputId": "a532cf25-a422-473a-be4e-b34b4bd72cf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: -c: line 1: syntax error near unexpected token `('\n",
            "/bin/bash: -c: line 1: `[Large confusion matrix](09_confusion_matrix.png)'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print((TP + TN) / float(TP + TN + FP + FN))\n",
        "print(metrics.accuracy_score(y_test, y_pred_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "La06bNO1Jjbb",
        "outputId": "1bd98337-ec25-49b6-e7c3-278d15c89947"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6770833333333334\n",
            "0.6770833333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print((FP + FN) / float(TP + TN + FP + FN))\n",
        "print(1 - metrics.accuracy_score(y_test, y_pred_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7h-Uhg1JJjoa",
        "outputId": "82d18558-a86f-4fd6-8fa9-8d485fdea70b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3229166666666667\n",
            "0.32291666666666663\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(TP / float(TP + FN))\n",
        "print(metrics.recall_score(y_test, y_pred_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84j2SXtmQwsV",
        "outputId": "41881ea6-ff1d-4d8d-b8ea-1cf5fd117dfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.25806451612903225\n",
            "0.25806451612903225\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(TN / float(TN + FP))\n",
        "# print(metrics.recall_score(y_test, y_pred_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LNVyHlEQw_H",
        "outputId": "aec6ae72-7cb0-4375-b341-c83a281e0323"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8769230769230769\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logreg.predict(X_test)[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zw81I9yyQxQo",
        "outputId": "5897a443-2c94-487a-e824-68d3cbe75b1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logreg.predict_proba(X_test)[0:10, :]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYrVVYKiQxi-",
        "outputId": "3d28a0c6-1e94-44a7-eebc-d25e059f6a00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.61405867, 0.38594133],\n",
              "       [0.7505398 , 0.2494602 ],\n",
              "       [0.74167648, 0.25832352],\n",
              "       [0.60291327, 0.39708673],\n",
              "       [0.88426611, 0.11573389],\n",
              "       [0.87695895, 0.12304105],\n",
              "       [0.50819992, 0.49180008],\n",
              "       [0.44582289, 0.55417711],\n",
              "       [0.77950769, 0.22049231],\n",
              "       [0.25853303, 0.74146697]])"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logreg.predict_proba(X_test)[0:10, 1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VFhM9cHyQxz3",
        "outputId": "c239eaa3-094b-4f95-d4b0-6a7b3ebdac0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.38594133, 0.2494602 , 0.25832352, 0.39708673, 0.11573389,\n",
              "       0.12304105, 0.49180008, 0.55417711, 0.22049231, 0.74146697])"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_prob = logreg.predict_proba(X_test)[:, 1]"
      ],
      "metadata": {
        "id": "lHHRZTXmQyCO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['font.size'] = 14"
      ],
      "metadata": {
        "id": "jvOKeWymQyUd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(y_pred_prob, bins=8)\n",
        "plt.xlim(0, 1)\n",
        "plt.title('Histogram of predicted probabilities')\n",
        "plt.xlabel('Predicted probability of diabetes')\n",
        "plt.ylabel('Frequency')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "C6ybumCFVHwW",
        "outputId": "d0a48f0b-fc24-458c-ffa1-3c6c777d7fbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "from sklearn.preprocessing import binarize\n",
        "y_pred_class = (y_pred_prob > 0.3).astype(int)  # Directly apply the threshold"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "WZ1LJve9cOw2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_prob[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7cYcMkCVKYu",
        "outputId": "b10f579c-77a5-46fe-8d2e-a13ecb932771"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.38594133, 0.2494602 , 0.25832352, 0.39708673, 0.11573389,\n",
              "       0.12304105, 0.49180008, 0.55417711, 0.22049231, 0.74146697])"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_class[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9b4NBhlzVKpK",
        "outputId": "5b5bea67-04ca-4be3-c239-9103c9067386"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 1, 0, 0, 1, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KcTQ8-hIVK4-",
        "outputId": "1f3479c3-967b-47c6-d1e9-8a15ce9bb566"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[114  16]\n",
            " [ 46  16]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(y_test, y_pred_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEs3mEeNVLIK",
        "outputId": "f99d9497-87c7-4786-b8b9-d27152e26e01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[82 48]\n",
            " [17 45]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_prob)\n",
        "plt.plot(fpr, tpr)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.rcParams['font.size'] = 14  # Increase font size\n",
        "plt.title('ROC curve for diabetes classifier')\n",
        "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
        "plt.ylabel('True Positive Rate (Sensitivity)')\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "gMTB94T5VLYI",
        "outputId": "bc3504bf-9ff8-406d-bfe9-2e2ee6211825"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_threshold(threshold):\n",
        "    print('Sensitivity:', tpr[thresholds > threshold][-1])\n",
        "    print('Specificity:', 1 - fpr[thresholds > threshold][-1])"
      ],
      "metadata": {
        "id": "7yyEmzqTVLo9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_threshold(0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TpB_HrDVL8U",
        "outputId": "9cf507f1-6f01-4cfc-ad5a-55d8bd5745b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sensitivity: 0.9838709677419355\n",
            "Specificity: 0.16153846153846152\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.roc_auc_score(y_test, y_pred_prob))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xf1J0swVMu-",
        "outputId": "f30dd8b6-6510-4fba-d206-5a5df32fe02b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7227047146401985\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DebjG9MNVM6_",
        "outputId": "42c0206e-8c44-482c-bbfd-cfbe47a5aaf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7425071225071225"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://bit.ly/kaggletrain')"
      ],
      "metadata": {
        "id": "t2TRlJtVVNHE"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BQZ1l3oVNTD",
        "outputId": "50a20a9f-80f5-4c19-c05f-33460faba51f"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36giAFWDVNeg",
        "outputId": "ff8fdef7-62e7-4522-a299-bfc695f1213a"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RooMmldoVNpK",
        "outputId": "7cd888d0-c9c1-4e15-f46b-d81de4c2503d"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.loc[df.Embarked.notna(), ['Survived', 'Pclass', 'Sex', 'Embarked']]"
      ],
      "metadata": {
        "id": "nBmDCW8fVOCp"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "alEiyNegNRF8",
        "outputId": "e5f52848-50da-4000-88e5-5112f86ecd9a"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(889, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.loc[:, ['Pclass']]\n",
        "y = df.Survived"
      ],
      "metadata": {
        "id": "Jh9dcbzTVOP7"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyA7nw58VPC0",
        "outputId": "4c88e824-393f-4835-b5f1-8c02d0878651"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(889, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(889,)"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logreg = LogisticRegression(solver='lbfgs')"
      ],
      "metadata": {
        "id": "GSSus8oKVPOp"
      },
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_score(logreg, X, y, cv=5, scoring='accuracy').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAxdq3YWVPZ9",
        "outputId": "72b78d1e-bcf0-4faf-ddb1-d65e1359a0a1"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6783406335301212"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDQT8WKWVQPf",
        "outputId": "b0270af1-f716-4086-e7ca-cb81f52905e5"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived\n",
              "0    0.617548\n",
              "1    0.382452\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ohe = OneHotEncoder(sparse = False)"
      ],
      "metadata": {
        "id": "cIZB4VisVQfw"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ohe.fit_transform(df[['Sex']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gzMs2HhSVQvc",
        "outputId": "82b4078b-0908-40c2-ed05-d98f6781eaa2"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1.],\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       ...,\n",
              "       [1., 0.],\n",
              "       [0., 1.],\n",
              "       [0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X =df.drop('Survived', axis='columns')\n"
      ],
      "metadata": {
        "id": "Ymxx-TG7VQ-U"
      },
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_42SK09CVRQa",
        "outputId": "c9963e69-7e30-4e6d-8286-6d46a61223c6"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass     Sex Embarked\n",
              "0       3    male        S\n",
              "1       1  female        C\n",
              "2       3  female        S\n",
              "3       1  female        S\n",
              "4       3    male        S"
            ],
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 889,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import make_column_transformer"
      ],
      "metadata": {
        "id": "se9wMqcQVRfn"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "column_trans = make_column_transformer(\n",
        "    (OneHotEncoder(), ['Sex', 'Embarked']),\n",
        "    remainder='passthrough'\n",
        "\n",
        "    )"
      ],
      "metadata": {
        "id": "mKENFgR4VSW0"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "column_trans.fit_transform(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GMixOs8VSlk",
        "outputId": "551fec35-b76d-47b2-c011-ecd378e2795b"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1., 0., 0., 1., 3.],\n",
              "       [1., 0., 1., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 1., 3.],\n",
              "       ...,\n",
              "       [1., 0., 0., 0., 1., 3.],\n",
              "       [0., 1., 1., 0., 0., 1.],\n",
              "       [0., 1., 0., 1., 0., 3.]])"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import make_pipeline"
      ],
      "metadata": {
        "id": "kfvpkOLCVS0f"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = make_pipeline(column_trans, logreg)"
      ],
      "metadata": {
        "id": "lPP_ElYzVTEg"
      },
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTZ8NWz6MmWl",
        "outputId": "c7bb6574-5afa-4598-ae51-822a78af7dd2"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7727924839713071"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = X.sample(5, random_state=99)\n",
        "X_new\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xynlqmd8Mmjq",
        "outputId": "483b92cb-5c66-4494-b2cd-a22119f5058d"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass     Sex Embarked\n",
              "599       1    male        C\n",
              "512       1    male        S\n",
              "273       1    male        C\n",
              "215       1  female        C\n",
              "790       3    male        Q"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Embarked</th>\n",
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              "      <th>599</th>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>790</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10327580-ae6b-414e-ba02-4399867a91c8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-10327580-ae6b-414e-ba02-4399867a91c8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-10327580-ae6b-414e-ba02-4399867a91c8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ed4b774b-0ac0-4889-9db5-f1fbf86c3639\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed4b774b-0ac0-4889-9db5-f1fbf86c3639')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed4b774b-0ac0-4889-9db5-f1fbf86c3639 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fc1358f6-3081-4ff9-bbd5-b0edac5b1ebc\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_new')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_fc1358f6-3081-4ff9-bbd5-b0edac5b1ebc button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_new');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_new",
              "summary": "{\n  \"name\": \"X_new\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"C\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "EOi2CzshQdOK",
        "outputId": "d8edea5e-5068-47cb-c532-3b579c09016a"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('onehotencoder',\n",
              "                                                  OneHotEncoder(),\n",
              "                                                  ['Sex', 'Embarked'])])),\n",
              "                ('logisticregression', LogisticRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(),\n",
              "                                                  [&#x27;Sex&#x27;, &#x27;Embarked&#x27;])])),\n",
              "                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(),\n",
              "                                                  [&#x27;Sex&#x27;, &#x27;Embarked&#x27;])])),\n",
              "                (&#x27;logisticregression&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
              "                                 [&#x27;Sex&#x27;, &#x27;Embarked&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Embarked&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Pclass&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.predict(X_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4K0YPmunMmvx",
        "outputId": "3eba7739-1dc1-4d9a-abc9-5768872111db"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8D_KrauMVTVa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}